While animals track or search for targets, sensory organs make small unexplained movements on top of the primary task-related motions. While multiple theories for these movements exist—in that they support infotaxis, gain adaptation, spectral whitening, and high-pass filtering—predicted trajectories show poor fit to measured trajectories. We propose a new theory for these movements called energy-constrained proportional betting, where the probability of moving to a location is proportional to an expectation of how informative it will be balanced against the movement’s predicted energetic cost. Trajectories generated in this way show good agreement with measured trajectories of fish tracking an object using electrosense, a mammal and an insect localizing an odor source, and a moth tracking a flower using vision. Our theory unifies the metabolic cost of motion with information theory. It predicts sense organ movements in animals and can prescribe sensor motion for robots to enhance performance.
Abstract. In this paper, with projection value being considered as fitness value, the Dynamic Multi-Swarm Particle Swarm Optimizer (DMS-PSO) is applied to improve the best atom searching problem in the Sparse Decomposition of image based on the Matching Pursuit (MP) algorithm. Furthermore, Discrete Coefficient Mutation (DCM) strategy is introduced to enhance the local searching ability of DMS-PSO in the MP approach over the anisotropic atom dictionary. Experimental results indicate the superiority of DMS-PSO with DCM strategy in contrast with other popular versions of PSO.Keywords: PSO, Matching Pursuit, Sparse Decomposition, Sparse Representation, DMS. IntroductionSparse Representation is becoming more and more popular, playing a pivotal role in signal and image processing based on its adaptivity, flexibility and sparsity. Mallat and Zhang[1] originally published the idea of Sparse Decomposition of a signal over an over-complete dictionary in 1993. Based on the redundancy of the over-complete dictionary, the sparse representation of an arbitrary signal is unique and concise. During the process of sparse representation, the evaluation of the matching degree is needed to ensure the precision and concision of the possible combination of atoms. However, it has been proven that finding the K-term ( K N , N represent the Dimension of the signal) representation for arbitrary signal on an over-complete dictionary is NP-Hard [2,3].Among ways to approach optimal sparse decomposition of a signal, Matching Pursuit (MP), originally proposed by Mallat and Zhang[1] in 1993, is one of the most widely used methods. Although MP's computational complexity is much less than other approaches like Basis Pursuit [4], it is still considerably high, which greatly hinders the application of Sparse Decomposition.
Sensory organs-be they independently movable like eyes or requiring whole body 10 movement as in the case of electroreceptors-are actively manipulated throughout 11 stimulus-driven behaviors. While multiple theories for these movements exist, such as infotaxis, 12 in those cases where they are sufficiently detailed to predict sensory organ trajectories, they 13 show poor fit to measured trajectories. Here we present evidence that during tracking, these 14 trajectories are predicted by energy-constrained proportional betting, where the probability of 15 moving a sense organ to a location is proportional to an estimate of how informative that 16 location will be combined with its energetic cost. Energy-constrained proportional betting 17 trajectories show good agreement with measured trajectories of four species engaged in visual, 18 olfactory, and electrosensory tracking tasks. Our approach combines information-theoretic 19 approaches in sensory neuroscience with analyses of the energetics of movement. It can predict 20 sense organ movements in animals and prescribe them in robotic tracking devices. 21 22 26 2010; Khan et al., 2012; Stamper et al., 2012; Catania, 2013; Sponberg et al., 2015; Lockey and 27 Willis, 2015; Rucci and Victor, 2015; Stockl et al., 2017) (Fig. 1). There are several models in the 28 literature that have been proposed (Stamper et al., 2012; Yovel et al., 2010; Khan et al., 2012; Rucci 29 and Victor, 2015; Najemnik and Geisler, 2005; Yang et al., 2016; Stockl et al., 2017). For example, 30in the related case of signal-emitter organ control, fruit bats are known to oscillate their tongue-31 click-based sonar signals on approach to their targets (Yovel et al., 2010). This can be effective 32 because for many signal sources, the signal intensity peaks at the target's location and tapers away 33 in all directions. The expected amount of information-in the bat study quantified by the Fisher 34 Information of the emitted sonar signal-is highest at the maximum slope of the signal profile 35 because at those locations, small variations in the emitter position leads to large changes in emitted 36 signal power on target and thus also in the returning echos. In contrast, at the flat peak of the 37 profile where the object is located, small variations in emitter position lead to small or no change 38 in signal and returning echo; the expected information is therefore low. For active sensing animals 39 like bats, dolphins, and electric fish, placing emitter organs so that the target is at a location of high 40 1 of 33 Manuscript submitted to eLife signal slope then leads to better information harvesting and hence better estimation of the target 41 location (Clarke et al., 2015;Yovel et al., 2010). The same is true for animals guided by light or 42 sound, through placement of sense organs at high slope locations. Puzzlingly, this would suggest 43 that animals should monitor an information peak (one location of high signal slope), while the 44 documented animal behavior suggests that they move bet...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.